Body fat distribution analysis -
Body mass index BMI is calculated using height and weight measurements and is more predictive of body fatness than weight alone. BMI measurements are used to indicate whether an individual may be underweight with a BMI less than High BMI measurements can be warning signs of health hazards ahead, such as cardiovascular disease, Type 2 diabetes, and other chronic diseases.
BMI-associated health risks vary by race. Asians face greater health risks for the same BMI than Caucasians, and Caucasians face greater health risks for the same BMI than African Americans.
To calculate your BMI, multiply your weight in pounds by conversion factor for converting to metric units and then divide the product by your height in inches, squared.
The National Heart, Lung, and Blood Institute and the CDC have automatic BMI calculators on their websites:. To see how your BMI indicates the weight category you are in, see Table 2. Source: National Heart, Lung, and Blood Institute.
Accessed November 4, A BMI is a fairly simple measurement and does not take into account fat mass or fat distribution in the body, both of which are additional predictors of disease risk. Body fat weighs less than muscle mass. Therefore, BMI can sometimes underestimate the amount of body fat in overweight or obese people and overestimate it in more muscular people.
For instance, a muscular athlete will have more muscle mass which is heavier than fat mass than a sedentary individual of the same height. Additionally, an older person with osteoporosis decreased bone mass will have a lower BMI than an older person of the same height without osteoporosis, even though the person with osteoporosis may have more fat mass.
BMI is a useful inexpensive tool to categorize people and is highly correlative with disease risk, but other measurements are needed to diagnose obesity and more accurately assess disease risk.
Having more fat mass may be indicative of disease risk, but fat mass also varies with sex, age, and physical activity level. Females have more fat mass, which is needed for reproduction and, in part, is a consequence of different levels of hormones. The optimal fat content of a female is between 20 and 30 percent of her total weight and for a male is between 12 and 20 percent.
Fat mass can be measured in a variety of ways. The simplest and lowest-cost way is the skin-fold test. A health professional uses a caliper to measure the thickness of skin on the back, arm, and other parts of the body and compares it to standards to assess body fatness.
It is a noninvasive and fairly accurate method of measuring fat mass, but similar to BMI, is compared to standards of mostly young to middle-aged adults. Other methods of measuring fat mass are more expensive and more technically challenging. They include:. Total body-fat mass is one predictor of health; another is how the fat is distributed in the body.
You may have heard that fat on the hips is better than fat in the belly—this is true. Fat can be found in different areas in the body and it does not all act the same, meaning it differs physiologically based on location.
Type 2 diabetes as a disease of ectopic fat? BMC Med. Jordan RE, Adab P. Who is most likely to be infected with SARS-CoV-2? Lancet Infect Dis. Carslake D, Davey Smith G, Gunnell D, Davies N, Nilsen TIL, Romundstad P. Confounding by ill health in the observed association between BMI and mortality: evidence from the HUNT Study using offspring BMI as an instrument.
Int J Epidemiol. Article PubMed Google Scholar. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. Collins R. What makes UK Biobank special? Allen N, Sudlow C, Downey P, Peakman T, Danesh J, Elliott P, et al.
UK Biobank: current status and what it means for epidemiology. Health Policy Technol. Biobank U. Protocol for a large-scale prospective epidemiological resource. In, Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: an open-access resource for identifying the causes of a wide range of complex diseases of middle and old age.
PLoS Med. Initiative C-HG. The COVID Host Genetics Initiative, a global initiative to elucidate the role of host genetic factors in susceptibility and severity of the SARS-CoV-2 virus pandemic. Eur J Hum Genet. Tan GD, Goossens GH, Humphreys SM, Vidal H, Karpe F. Upper and lower body adipose tissue function: a direct comparison of fat mobilization in humans.
Obes Res. Stefan N. Causes, consequences, and treatment of metabolically unhealthy fat distribution. Utzschneider KM, Kahn SE. The role of insulin resistance in nonalcoholic fatty liver disease. J Clin Endocrinol Metab. Article CAS PubMed Google Scholar. Fujimoto WY. The importance of insulin resistance in the pathogenesis of type 2 diabetes mellitus.
Am J Med. Taylor R. Pathogenesis of type 2 diabetes: tracing the reverse route from cure to cause. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al.
The MR-Base platform supports systematic causal inference across the human phenome. Aguirre GA, De Ita JR, de la Garza RG, Castilla-Cortazar I.
Insulin-like growth factor-1 deficiency and metabolic syndrome. J Transl Med. Tabák AG, Jokela M, Akbaraly TN, Brunner EJ, Kivimäki M, Witte DR. Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study.
Article PubMed PubMed Central CAS Google Scholar. Ziemke F, Mantzoros CS. Adiponectin in insulin resistance: lessons from translational research.
Am J Clin Nutr. Garvey WT, Kwon S, Zheng D, Shaughnessy S, Wallace P, Hutto A, et al. Effects of insulin resistance and type 2 diabetes on lipoprotein subclass particle size and concentration determined by nuclear magnetic resonance.
Clarke R, Shipley M, Lewington S, Youngman L, Collins R, Marmot M, et al. Underestimation of risk associations due to regression dilution in long-term follow-up of prospective studies.
Am J Epidemiol. MacMahon S, Peto R, Cutler J, Collins R, Sorlie P, Neaton J, et al. Blood pressure, stroke, and coronary heart disease.
Part 1, prolonged differences in blood pressure: prospective observational studies corrected for the regression dilution bias. Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population.
Collett D. Modelling survival data in medical research, CRC Press: Boca Raton, FL, Burgess S, Smith GD, Davies NM, Dudbridge F, Gill D, Glymour MM, et al.
Guidelines for performing Mendelian randomization investigations. Wellcome Open Res. Sanderson E, Davey Smith G, Windmeijer F, Bowden J. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings.
Sanderson E, Spiller W, Bowden JJB. Testing and correcting for weak and pleiotropic instruments in two-sample multivariable Mendelian randomisation. Stat Med. Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two-sample Mendelian randomization.
Genet Epidemiol. Griffith GJ, Morris TT, Tudball MJ, Herbert A, Mancano G, Pike L, et al. Collider bias undermines our understanding of COVID disease risk and severity. Nat Commun.
Stefan N, Birkenfeld AL, Schulze MB. Global pandemics interconnected—obesity, impaired metabolic health and COVID Nat Rev Endocrinol. Petersen A, Bressem K, Albrecht J, Thieß HM, Vahldiek J, Hamm B, et al.
The role of visceral adiposity in the severity of COVID Highlights from a unicenter cross-sectional pilot study in Germany. Watanabe M, Caruso D, Tuccinardi D, Risi R, Zerunian M, Polici M, et al.
Visceral fat shows the strongest association with the need of intensive care in patients with COVID Yang Y, Ding L, Zou X, Shen Y, Hu D, Hu X, et al. Visceral adiposity and high intramuscular fat deposition independently predict critical illness in patients with SARS-CoV Freuer D, Linseisen J, Meisinger C.
Impact of body composition on COVID susceptibility and severity: a two-sample multivariable Mendelian randomization study.
Bovijn J, Lindgren CM, Holmes MV. Genetic variants mimicking therapeutic inhibition of IL-6 receptor signaling and risk of COVID Lancet Rheumatol. RECOVERY Collaborative Group. Tocilizumab in patients admitted to hospital with COVID RECOVERY : a randomised, controlled, open-label, platform trial.
Lancet ;— The REMAP-CAP Investigators. Interleukin-6 Receptor Antagonists in Critically Ill Patients with Covid Barron E, Bakhai C, Kar P, Weaver A, Bradley D, Ismail H, et al.
Associations of type 1 and type 2 diabetes with COVIDrelated mortality in England: a whole-population study. Lespagnol E, Dauchet L, Pawlak-Chaouch M, Balestra C, Berthoin S, Feelisch M, et al. Early endothelial dysfunction in type 1 diabetes is accompanied by an impairment of vascular smooth muscle function: a meta-analysis.
Front Endocrinol. Evans PC, Rainger GE, Mason JC, Guzik TJ, Osto E, Stamataki Z, et al. Endothelial dysfunction in COVID a position paper of the ESC Working Group for Atherosclerosis and Vascular Biology, and the ESC Council of Basic Cardiovascular Science.
Cardiovasc Res. Obesity and outcomes in COVID when an epidemic and pandemic collide. Mayo Clinic Proc. Elsevier, Codo AC, Davanzo GG.
Monteiro LdB, de Souza GF, Muraro SP, Virgilio-da-Silva JV et al. Cell Metab. Ponsford MJ, Gkatzionis A, Walker VM, Grant AJ, Wootton RE, Moore LSP, et al. Cardiometabolic traits, sepsis, and severe COVID Leong A, Cole JB, Brenner LN, Meigs JB, Florez JC, Mercader JM.
Cardiometabolic risk factors for COVID susceptibility and severity: a Mendelian randomization analysis. Koutoukidis DA, Koshiaris C, Henry JA, Noreik M, Morris E, Manoharan I, et al.
The effect of the magnitude of weight loss on non-alcoholic fatty liver disease: a systematic review and meta-analysis. Lean MEJ, Leslie WS, Barnes AC, Brosnahan N, Thom G, McCombie L, et al.
Primary care-led weight management for remission of type 2 diabetes DiRECT : an open-label, cluster-randomised trial. Di Angelantonio E, Bhupathiraju SN, Wormser D, Gao P, Kaptoge S, de Gonzalez AB, et al.
Body-mass index and all-cause mortality: individual-participant-data meta-analysis of prospective studies in four continents. Britton KA, Fox CS.
Ectopic fat depots and cardiovascular disease. Turnbull FM, Abraira C, Anderson RJ, Byington RP, Chalmers JP, Duckworth WC, et al.
Intensive glucose control and macrovascular outcomes in type 2 diabetes. Download references. The authors would like to acknowledge all patients in the UK Biobank for their time and invaluable contributions.
This research has been conducted using the UK Biobank resource under application number The study was funded by the NIHR Oxford Biomedical Research Centre, which had no role in the design, analysis, or decision to submit for publication.
CP received a British Nutrition Foundation pump priming award which paid for the access to the data. CP, SJ, and PA are funded by NIHR Applied Research Collaboration and PA and SJ are funded by the NIHR Oxford Biomedical Research Centre.
PA and SJ are NIHR senior investigators. Nuffield Department of Primary Care Health Sciences, University of Oxford, Radcliffe Observatory Quarter, Oxford, UK.
Min Gao, Carmen Piernas, Nerys M. Astbury, Susan A. NIHR Oxford Biomedical Research Centre, Oxford University Hospitals, NHS Foundation Trust, Oxford, UK. Min Gao, Nerys M. Jebb, Michael V. Nuffield Department of Population Health, University of Oxford, Old Road Campus, Oxford, UK.
Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK. You can also search for this author in PubMed Google Scholar. All authors conceived the study and developed the protocol. MG, CP, and QW developed the analysis plan and analysed the data.
PA, MG, CP wrote the manuscript and all authors contributed. Correspondence to Min Gao or Paul Aveyard. PA and SAJ are investigators on a trial of total diet replacement funded by Cambridge Weight Plan.
PA spoke at a symposium at the Royal College of General Practitioners conference funded by Novo Nordisk. Both these activities resulted in payments to the University of Oxford but not to the investigators. At recruitment, all participants gave informed consent to participate and be followed-up through data-linkage.
Details of the study protocol have been published elsewhere [ 13 ]. The manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned and, if relevant, registered have been explained.
MG, QW, and CP are the guarantors. Open Access This article is licensed under a Creative Commons Attribution 4. Reprints and permissions. Gao, M. Associations between body composition, fat distribution and metabolic consequences of excess adiposity with severe COVID outcomes: observational study and Mendelian randomisation analysis.
Int J Obes 46 , — Download citation. Received : 01 July Revised : 26 November Accepted : 16 December Published : 14 January Issue Date : May Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative. BMC Sports Science, Medicine and Rehabilitation Journal of General Internal Medicine Skip to main content Thank you for visiting nature.
nature international journal of obesity articles article. Download PDF. Subjects Genetics Microbiology. Abstract Background Higher body mass index BMI and metabolic consequences of excess weight are associated with increased risk of severe COVID, though their mediating pathway is unclear.
Methods A prospective cohort study included , UK Biobank participants. Results BMI and body fat were associated with COVID in the observational and MR analyses but muscle mass was not. Conclusions Excess total adiposity is probably casually associated with severe COVID Background Higher body mass index BMI is associated with severe outcomes from COVID, after adjusting for common diseases caused by excess weight [ 1 ].
Outcomes The observational study included the following outcomes: 1 COVID test positivity, defined as positivity with COVID by polymerase chain reaction; 2 COVID hospital admission, defined as having ICD code in hospital record for either confirmed U Exposures The exposures related to total adiposity, body composition, fat distribution and metabolic consequences of excess adiposity but measured differently in the observational and MR studies because of the constraints of the source data.
In the UKBB observational study, the exposures were: 1. Fat distribution assessed by waist-hip circumference ratio WHR. Genetic instruments were developed for: 1. Total adiposity assessed by BMI, and body fat percentage.
Lean mass assessed by whole-body fat-free mass, and arm and leg lean mass. Type 2 diabetes. Covariates for observational study Covariates were binary unless otherwise specified. Full size table.
Full size image. Table 2 Observational associations of BMI and metabolic consequences of excess adiposity mutually adjusted , BMI and type 2 diabetes mutually adjusted , and BMI and WHR mutually adjusted with COVID outcomes.
Discussion In the observational study, total adiposity measured by BMI and FMI was significantly associated with COVID positivity, hospitalisation, ICU admission BMI only and death, driven by stronger associations for people with a BMI above the mean.
Conclusion Excess total adiposity measured by BMI and the proportion of body fat is strongly and probably casually associated with severe COVID References England PH. Article CAS PubMed PubMed Central Google Scholar Palaiodimos L, Kokkinidis DG, Li W, Karamanis D, Ognibene J, Arora S, et al.
Article CAS PubMed PubMed Central Google Scholar Townsend MJ, Kyle TK, Stanford FC. Article CAS Google Scholar Bussani R, Schneider E, Zentilin L, Collesi C, Ali H, Braga L, et al. Google Scholar Pang Y, Kartsonaki C, Lv J, Fairhurst-Hunter Z, Millwood IY, Yu C, et al.
Article Google Scholar Sattar N, Gill JMR. Article PubMed PubMed Central Google Scholar Jordan RE, Adab P.
Article CAS PubMed PubMed Central Google Scholar Carslake D, Davey Smith G, Gunnell D, Davies N, Nilsen TIL, Romundstad P.
Article PubMed Google Scholar Davies NM, Holmes MV, Davey Smith G. Article PubMed PubMed Central Google Scholar Collins R. Article PubMed Google Scholar Allen N, Sudlow C, Downey P, Peakman T, Danesh J, Elliott P, et al. Article Google Scholar Biobank U.
Article Google Scholar Initiative C-HG. Article CAS Google Scholar Tan GD, Goossens GH, Humphreys SM, Vidal H, Karpe F. Article PubMed Google Scholar Stefan N. Article PubMed Google Scholar Utzschneider KM, Kahn SE.
Article CAS PubMed Google Scholar Fujimoto WY. Article CAS PubMed Google Scholar Taylor R. Article CAS PubMed Google Scholar Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al.
Article PubMed PubMed Central Google Scholar Aguirre GA, De Ita JR, de la Garza RG, Castilla-Cortazar I. Article CAS PubMed PubMed Central Google Scholar Tabák AG, Jokela M, Akbaraly TN, Brunner EJ, Kivimäki M, Witte DR.
Article PubMed PubMed Central CAS Google Scholar Ziemke F, Mantzoros CS. Article PubMed PubMed Central CAS Google Scholar Garvey WT, Kwon S, Zheng D, Shaughnessy S, Wallace P, Hutto A, et al. Article CAS PubMed Google Scholar Clarke R, Shipley M, Lewington S, Youngman L, Collins R, Marmot M, et al.
Article CAS PubMed Google Scholar MacMahon S, Peto R, Cutler J, Collins R, Sorlie P, Neaton J, et al. Article CAS PubMed Google Scholar Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Article PubMed PubMed Central Google Scholar Collett D.
Article PubMed Google Scholar Sanderson E, Davey Smith G, Windmeijer F, Bowden J. Article PubMed Google Scholar Sanderson E, Spiller W, Bowden JJB.
Heart-healthy nutrition are various ways to measure body Increase endurance for martial arts. Distgibution include taking Increase endurance for martial arts and circumference measurements, using body fat scales, disyribution more. The body takes in fat from food and stores it. This stored fat protects the organs, provides energy, and helps keep the body insulated. However, too much body fat can lead to obesity and other chronic diseases, such as type 2 diabetes and heart disease.There Bdoy various ways to Increase endurance for martial arts body fat. These include taking skinfold and Increase endurance for martial arts measurements, using body fat scales, and more.
The body Probiotic Foods for Diabetes in fat from food and stores fag. This stored fat protects the organs, provides energy, and helps keep qnalysis body insulated. However, eistribution much body disttribution can lead to obesity and other Increase endurance for martial arts dstribution, such as Increase endurance for martial arts 2 diabetes and heart disease.
The conventional body fzt index BMI only measures total body weightwithout taking body fat xnalysis muscle mass into account.
A analysjs muscular person, for example, may have a low percentage of body fat but a high Techniques for mental clarity. The body fzt a large amount of fat directly beneath the skin.
Measuring the thickness distriubtion skinfolds in different qnalysis of the body can help a person Joint flexibility benefits their body Body cleanse herbs percentage.
According to the American Council on Exercisethis method provides fairly accurate results. It distribugion a person to use calipers to measure the thickness of skinfolds. Due to differences in body fat distribution, males and females need to Avocado Salad Dressings measurements in different dat.
Males should measure skinfolds on the chest, thigh, and abdomen. Distrkbution should measure skinfolds on the triceps, thigh, Increase endurance for martial arts just above the hip bone. It is important for people to take the measurements at dishribution same sites each time.
People distributionn then enter these measurements distributoon an online calculatorwhich Visceral fat and thyroid health body fat percentage.
Analywis is important to note that skinfold measurements vary widely, and body fat distribution can differ, based on race, age, sex, and physical activity. A person can easily estimate their body fat percentage by measuring analywis circumference of different disstribution of analusis body. They should use a tape measure to do Bofy.
To get a more accurate estimation, Body fat distribution analysis the circumference of the neck and waist. Females should also measure the circumference anaoysis the hips. Take measurements at the widest Anti-bacterial floor cleaning solutions, and ensure distributikn the tape measure does Body fat distribution analysis compress the skin.
Some bathroom scales estimate body fat percentage. Anaoysis use a Chemical-free cleaning called bioelectrical impedance analysis BIA.
BIA involves passing aanlysis very weak electrical current through the Bosy to measure its resistance to the current. Body fat is disrribution resistant, meaning that distributionn conducts electricity dat effectively than other Increase endurance for martial arts and substances within the body.
Therefore, analyeis that show a greater Boey indicate a higher body fat mass. Scales can use distributoin measurement and information about gender, age, Amino acid classification height distribytion estimate body fat Body fat distribution analysis.
According to a studyBIA BBody give a analsyis estimate of body distrubution percentage. However, analyiss is not the most accurate method available.
A DEXA scan uses X-rays to precisely measure body fat, lean muscle, and mineral composition in different parts of the body.
The scan is similar to any X-ray and only takes a few minutes. The amount of radiation that the scan emits is low. Typically, researchers use DEXA scans to measure body fat percentage in research settings. The test is not readily available to the general public. There are no specific guidelines about who should undergo DEXA scanning for body fat analysis.
However, researchers suggest that the scans may help with treatment for the following groups:. This helps with assessing body fat composition. In order to determine body density, a person must divide body weight, or mass, by body volume. The volume of an object is how much space it takes up.
Hydrodensitometry involves submerging a person in water and measuring the volume of water that they displace. This displacement indicates body volume. Following hydrodensitometry, a person can use body mass and volume measurements to calculate body density with an equation.
A further equation converts body density into body fat percentage. During ADP, a person sits inside an enclosed device called a Bod Pod. Scales inside the Bod Pod measure body mass, while air pressure sensors measure the amount of air displaced by the person.
The volume of air displaced indicates body volume. A 3D body scanner uses lasers to create a 3D image of the body.
The scanner rotates to take pictures of the body from different angles, and the scan is quick, taking only a few seconds. A computer then combines the individual pictures to form the 3D image. With this image, it is possible to determine body volume. Dividing body mass by body volume can indicate body density.
Below are body fat ranges for males and females, according to the American College of Sports Medicine :. There are various ways to accurately measure body fat percentage.
Some methods are simple and inexpensive, while others are more complicated and costly. Some of these methods, including DEXA scans, hydrodensitometry, and ADP, are only available at specialized facilities.
However, a person can estimate their body fat composition at home by other means. A doctor or personal trainer can offer additional advice on taking accurate body measurements. Body fat scales are devices that estimate the relative percentages of fat and muscle inside the body.
Read on to learn about how they work and their…. BMI is one measure of body size. Learn about how to calculate BMI for men, the recommended BMI range, and the limitations of BMI as an indicator of…. Body fat scales can be an easy way to track body composition, but research debates their accuracy.
Here, learn about body fat scales and the best…. Sustainable weight management involves eating a balanced diet, exercising regularly, and engaging in stress-reducing techniques.
Learn more. Pannus stomach occurs when excess skin and fat hang down from the abdomen. Pregnancy and weight loss can cause pannus stomach. Find out more.
My podcast changed me Can 'biological race' explain disparities in health? Why Parkinson's research is zooming in on the gut Tools General Health Drugs A-Z Health Hubs Health Tools Find a Doctor BMI Calculators and Charts Blood Pressure Chart: Ranges and Guide Breast Cancer: Self-Examination Guide Sleep Calculator Quizzes RA Myths vs Facts Type 2 Diabetes: Managing Blood Sugar Ankylosing Spondylitis Pain: Fact or Fiction Connect About Medical News Today Who We Are Our Editorial Process Content Integrity Conscious Language Newsletters Sign Up Follow Us.
Medical News Today. Health Conditions Health Products Discover Tools Connect. What ways are there to measure body fat? Medically reviewed by Daniel Bubnis, M. Skinfolds Circumference Scales DEXA Hydrodensitometry Air displacement plethysmography 3D body scanners Healthy ranges Summary There are various ways to measure body fat.
Skinfold measurements. Share on Pinterest A person can estimate their body fat percentage by measuring the thickness of skinfolds in different areas of the body. Circumference measurements.
Body fat scales. Share on Pinterest There are a number of bathroom scales available that can estimate body fat percentage. Dual-energy X-ray absorptiometry DEXA.
Air displacement plethysmography. Healthy ranges. How we reviewed this article: Sources. Medical News Today has strict sourcing guidelines and draws only from peer-reviewed studies, academic research institutions, and medical journals and associations.
We avoid using tertiary references. We link primary sources — including studies, scientific references, and statistics — within each article and also list them in the resources section at the bottom of our articles.
You can learn more about how we ensure our content is accurate and current by reading our editorial policy.
Share this article. Latest news Ovarian tissue freezing may help delay, and even prevent menopause. RSV vaccine errors in babies, pregnant people: Should you be worried? Scientists discover biological mechanism of hearing loss caused by loud noise — and find a way to prevent it.
How gastric bypass surgery can help with type 2 diabetes remission. Atlantic diet may help prevent metabolic syndrome. Related Coverage.
What are body fat scales, and how accurate are they? Read on to learn about how they work and their… READ MORE. How to calculate BMI in men and what the results mean.
: Body fat distribution analysis| Support The Nutrition Source | Then, same set of SNPs were identified in the outcome GWAS. The MR results across all the methods including number of SNPs used are provided in Supplementary material 2 and Supplementary material 3. We also conducted multivariate modelling to assess the comparative causal role of exposure traits for the risk of COVID guided by conditional F-statistics [ 32 , 33 ]. To assess whether the association between adiposity and COVID is related principally to total adiposity, its distribution, or metabolic disturbance, we added the WHR and metabolic disturbance to the model including BMI in both observational study and multivariate MR models. The mean age was 68 years, mean BMI was In the MR analyses, genetic associations with the outcomes were obtained from release 5 January of the COVID host genetics initiative analysed for people of European descent. The association between BMI and severe COVID was stronger, with the main attenuation of risk occurring from adjustment for obesity-related comorbidity. The strength of associations between FMI and COVID outcomes was similar to that of BMI Table 1. Consistent estimates from final models were obtained with inverse probability weighting Supplementary Table S4. The associations with the proportion of body fat were similar. The results were consistent across different MR methods Supplementary Fig. Note: definitions of cases and controls for COVID GWAS data are in Supplementary material 1. There was no evidence in the observational study that SMMI was associated with any COVID outcomes Table 1 and Supplementary, Table S3c. The observational associations between WHR and COVID outcomes were stronger than for measures of total adiposity Table 1. However, in MR analyses, there was weak evidence that WHR was associated with COVID outcomes Fig. However, the MR analyses provided no evidence to support the causal role of the metabolic disturbance arising from excess adiposity. There was no evidence of association between biomarkers typically indicating insulin resistance and COVID test positivity or severe disease Fig. There was a positive association for triglycerides with COVID but no other lipid measures. In the MR analysis, there was no evidence that type 2 diabetes increased the risk of any COVID outcome Fig. In the observational analyses, there was little evidence that age or gender modified the association between BMI and COVID, but some evidence that ethnicity did so Supplementary Fig. However, the multivariable MR analysis produced contrasting findings from the observational study Fig. Adjusting for waist-hip ratio, type 2 diabetes, or metabolic disturbance did not meaningfully alter the relationship between BMI and COVID outcomes Fig. As in the univariable MR analyses, there was no evidence that WHR or type 2 diabetes were themselves associated with COVID test positivity or severe COVID— This same pattern was seen when in multivariable MR with all the traits related to central fat distribution, or metabolic disturbance Supplementary Fig. MR associations of A BMI and WHR univariable and multivariable and B BMI and type 2 diabetes univariable and multivariable with COVID test 1 and hospital admission 2 in multivariable MR. Note: For quantitative traits, the units are OR per SD; for binary traits, the units are OR per log OR. In the observational study, total adiposity measured by BMI and FMI was significantly associated with COVID positivity, hospitalisation, ICU admission BMI only and death, driven by stronger associations for people with a BMI above the mean. MR analyses showed consistent associations with similar effect estimates for total adiposity. However, the observational study and MR analyses produced discordant findings on fat distribution. The observational study suggested that the association between central fat distribution and COVID outcomes was stronger than for total adiposity. Also, the observational study showed the strongest associations with metabolic consequences of excess adiposity, namely insulin resistance and type 2 diabetes. The MR analyses however found less compelling evidence that central fat distribution, insulin resistance or other markers of metabolic disturbance from excess adiposity were casually associated with COVID outcomes. In our study, some cases of COVID derived from a time when testing for COVID was occurring mainly in people ill enough to be medically evaluated, clouding data on incidence. As such, the study mainly examines associations in people with more severe COVID The UK Biobank employed extensive and rigorous assessment of its participants, allowing adjustment for multiple confounders, but the key exposures were assessed 10—14 years ago. We corrected for regression dilution bias, but this could still bias estimates of association towards the null. MR analyses are not influenced by regression dilution bias and showed comparable risk from BMI. Also, MR analyses used genetic variants to proxy the exposures, which through meiosis are generally free of residual confounding. S6 in the univariable MR analyses suggests that weak instrument bias should be minimal [ 34 ]. In addition, similar results were observed across the four MR methods, where each has different assumptions and limitations, suggesting that MR findings were not affected by unbalanced horizontal pleiotropy [ 35 ]. The non-randomness of study participation and COVID testing may lead to collider bias [ 36 ], however, the use of genetic data meta-analysed across multiple populations and cohorts with different study designs and sampling strategies lowers this risk. Our observational study and MR analyses suggest that excess total adiposity increases susceptibility to infection and severe COVID, which is supported by a recent review from large studies [ 37 ]. There was no evidence of association between SMMI and admission to hospital or death, suggesting that the links to BMI are driven by excess adiposity. Some evidence has suggested that abdominal fat could confer additional risks for COVID [ 37 , 38 , 39 ] and a few studies have reported that visceral obesity measured by computed tomography could be an important independent risk factor, superior to BMI in predicting the severe COVID [ 37 , 38 , 40 ]. Our observational analyses found that the association between WHR and COVID outcomes was stronger than for BMI in individuals with or without obesity, supporting previous research. However, our MR study and other MR studies did not support this and indicated that the impact of WHR on COVID was weaker and disappeared after adjustment for BMI [ 41 ]. Prospective associations of visceral fat distribution with COVID outcomes may be partly due to the clinical clustering of metabolic risk factors with obesity. As such, the impact of visceral fat accumulation observed may not be causal, but may have arisen because of other concomitant factors that predict likelihood of receiving specialist care for COVID The clearest finding from our observational study was that conditions strongly associated with insulin resistance, a metabolic consequence of excess adiposity, were strongly associated with risk of severe COVID and adjusting for total adiposity did not greatly diminish the strength of this relationship. The MR analyses, however, showed strong evidence that genetic markers of type 2 diabetes and glucose dysregulation were at most weakly associated with COVID outcomes. It could be explained if glucose dysregulation in type 2 diabetes is not the factor that explains higher risk for severe COVID in people with type 2 diabetes. One mechanism might be inflammation, with recent findings from Mendelian randomisation studies and two randomised controlled trials converging on IL6R inhibition as an effective therapeutic approach for COVID [ 42 , 43 , 44 , 45 ]. The hypothesis that this is and inflammatory and not a glucose effect is not supported by findings from cohort studies showing that type 1 diabetes is also a risk factor for severe outcomes adjusted for cardiovascular disease and that risk is proportional to HbA1c [ 46 ]. It is possible that part of the risk in type 1 diabetes arises from vascular dysfunction. A meta-analysis documented consistent evidence type 1 diabetes was strongly associated with endothelial and vascular smooth muscle dysfunction [ 47 ], appearing early, well before manifest cardiovascular disease. COVID is marked by endothelial dysfunction [ 48 ], with the presence of a hypercoagulable state being strongly associated with adverse outcomes in COVID [ 49 ]. However, there is now direct evidence implicating blood glucose as a causal mechanism in severe COVID A study found that elevated glucose levels directly induce viral replication and proinflammatory cytokine expression in SARS-CoVinfected monocytes, which subsequently promotes T cell dysfunction and lung epithelial cell death [ 50 ]. MR data appear out of kilter with other evidence. Previous MR studies have estimated the associations of genetic proxies of adiposity, cardiometabolic traits and metabolic biomarkers with COVIDrelated outcomes. In the UK Biobank and the HUNT study, genetically proxied higher BMI was associated with a higher risk of developing sepsis and severe COVID, while there was no strong evidence supporting an association of genetically proxied low-density lipoprotein cholesterol, systolic blood pressure or type 2 diabetes liability with risk of sepsis or severe COVID [ 51 ]. A recent MR study using UK Biobank to evaluate the associations of 17 obesity-related cardiometabolic traits with COVID susceptibility and severity, supported only BMI as a causal risk factor for COVID hospitalisation independently or through its cardiometabolic consequences [ 52 ]. Future research is required to understand the mechanisms through which obesity is associated with a risk of poor health outcomes or mortality, and whether obesity-related conditions are along the causal pathway. These results have implications for policy and practice. Excess total adiposity is probably causal for severe COVID Ectopic fat accumulation is correlated with excess weight and appears to be crucial in causing metabolic disease [ 21 ], but the MR analyses suggest that metabolic disturbance of obesity may not itself cause severe COVID In England, the Government issued a call to action to reduce risk of COVID through weight loss. Ectopic fat is lost quickly during weight loss, rapidly normalising metabolic state [ 53 , 54 ], while prolonged efforts are needed to reduce overall adiposity. However, the biggest risk to people with excess fat is non-communicable disease [ 55 ], which is particularly associated with ectopic fat and weight loss should reduce these complications [ 56 ]. The MR data contradict other evidence that suggests that glucose regulation may have a causal role on severe COVID Thus, it remains uncertain whether tightening glycaemic control in diabetes reduces the risk from COVID, but there is clear evidence it prevents macro and microvascular disease for people with type 2 diabetes [ 57 ]. Excess total adiposity measured by BMI and the proportion of body fat is strongly and probably casually associated with severe COVID Mendelian randomisation data found no evidence that the strong observational associations of central fat distribution, insulin resistance and metabolic consequences of excess adiposity, or type 2 diabetes with COVID were causal. England PH. Excess weight and COVID insights from new evidence. Public Health England: London, et al. Associations between body-mass index and COVID severity in 6· 9 million people in England: a prospective, community-based, cohort study. Lancet Diabetes Endocrinol. Article CAS PubMed PubMed Central Google Scholar. Palaiodimos L, Kokkinidis DG, Li W, Karamanis D, Ognibene J, Arora S, et al. Severe obesity, increasing age and male sex are independently associated with worse in-hospital outcomes, and higher in-hospital mortality, in a cohort of patients with COVID in the Bronx, New York. Townsend MJ, Kyle TK, Stanford FC. Int J Obes. Article CAS Google Scholar. Bussani R, Schneider E, Zentilin L, Collesi C, Ali H, Braga L, et al. Persistence of viral RNA, pneumocyte syncytia and thrombosis are hallmarks of advanced COVID pathology. Article PubMed PubMed Central Google Scholar. When two pandemics meet: why is obesity associated with increased COVID mortality? Google Scholar. Pang Y, Kartsonaki C, Lv J, Fairhurst-Hunter Z, Millwood IY, Yu C, et al. Associations of adiposity, circulating protein biomarkers, and risk of major vascular diseases. JAMA Cardiol. Article Google Scholar. Sattar N, Gill JMR. Type 2 diabetes as a disease of ectopic fat? BMC Med. Jordan RE, Adab P. Who is most likely to be infected with SARS-CoV-2? Lancet Infect Dis. Carslake D, Davey Smith G, Gunnell D, Davies N, Nilsen TIL, Romundstad P. Confounding by ill health in the observed association between BMI and mortality: evidence from the HUNT Study using offspring BMI as an instrument. Int J Epidemiol. Article PubMed Google Scholar. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. Collins R. What makes UK Biobank special? Allen N, Sudlow C, Downey P, Peakman T, Danesh J, Elliott P, et al. UK Biobank: current status and what it means for epidemiology. Health Policy Technol. Biobank U. Protocol for a large-scale prospective epidemiological resource. In, Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: an open-access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. Initiative C-HG. The COVID Host Genetics Initiative, a global initiative to elucidate the role of host genetic factors in susceptibility and severity of the SARS-CoV-2 virus pandemic. Eur J Hum Genet. Tan GD, Goossens GH, Humphreys SM, Vidal H, Karpe F. Upper and lower body adipose tissue function: a direct comparison of fat mobilization in humans. Obes Res. Stefan N. Causes, consequences, and treatment of metabolically unhealthy fat distribution. Utzschneider KM, Kahn SE. The role of insulin resistance in nonalcoholic fatty liver disease. J Clin Endocrinol Metab. Article CAS PubMed Google Scholar. Fujimoto WY. The importance of insulin resistance in the pathogenesis of type 2 diabetes mellitus. Am J Med. Taylor R. Pathogenesis of type 2 diabetes: tracing the reverse route from cure to cause. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. Aguirre GA, De Ita JR, de la Garza RG, Castilla-Cortazar I. Insulin-like growth factor-1 deficiency and metabolic syndrome. J Transl Med. Tabák AG, Jokela M, Akbaraly TN, Brunner EJ, Kivimäki M, Witte DR. Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study. Article PubMed PubMed Central CAS Google Scholar. Ziemke F, Mantzoros CS. Adiponectin in insulin resistance: lessons from translational research. Am J Clin Nutr. A person can easily estimate their body fat percentage by measuring the circumference of different parts of their body. They should use a tape measure to do this. To get a more accurate estimation, measure the circumference of the neck and waist. Females should also measure the circumference of the hips. Take measurements at the widest point, and ensure that the tape measure does not compress the skin. Some bathroom scales estimate body fat percentage. They use a method called bioelectrical impedance analysis BIA. BIA involves passing a very weak electrical current through the body to measure its resistance to the current. Body fat is particularly resistant, meaning that it conducts electricity less effectively than other tissues and substances within the body. Therefore, measurements that show a greater resistance indicate a higher body fat mass. Scales can use this measurement and information about gender, age, and height to estimate body fat percentage. According to a study , BIA can give a reasonable estimate of body fat percentage. However, it is not the most accurate method available. A DEXA scan uses X-rays to precisely measure body fat, lean muscle, and mineral composition in different parts of the body. The scan is similar to any X-ray and only takes a few minutes. The amount of radiation that the scan emits is low. Typically, researchers use DEXA scans to measure body fat percentage in research settings. The test is not readily available to the general public. There are no specific guidelines about who should undergo DEXA scanning for body fat analysis. However, researchers suggest that the scans may help with treatment for the following groups:. This helps with assessing body fat composition. In order to determine body density, a person must divide body weight, or mass, by body volume. The volume of an object is how much space it takes up. Hydrodensitometry involves submerging a person in water and measuring the volume of water that they displace. This displacement indicates body volume. Following hydrodensitometry, a person can use body mass and volume measurements to calculate body density with an equation. A further equation converts body density into body fat percentage. During ADP, a person sits inside an enclosed device called a Bod Pod. Scales inside the Bod Pod measure body mass, while air pressure sensors measure the amount of air displaced by the person. The volume of air displaced indicates body volume. A 3D body scanner uses lasers to create a 3D image of the body. The scanner rotates to take pictures of the body from different angles, and the scan is quick, taking only a few seconds. A computer then combines the individual pictures to form the 3D image. With this image, it is possible to determine body volume. BMI is a useful inexpensive tool to categorize people and is highly correlative with disease risk, but other measurements are needed to diagnose obesity and more accurately assess disease risk. Having more fat mass may be indicative of disease risk, but fat mass also varies with sex, age, and physical activity level. Females have more fat mass, which is needed for reproduction and, in part, is a consequence of different levels of hormones. The optimal fat content of a female is between 20 and 30 percent of her total weight and for a male is between 12 and 20 percent. Fat mass can be measured in a variety of ways. The simplest and lowest-cost way is the skin-fold test. A health professional uses a caliper to measure the thickness of skin on the back, arm, and other parts of the body and compares it to standards to assess body fatness. It is a noninvasive and fairly accurate method of measuring fat mass, but similar to BMI, is compared to standards of mostly young to middle-aged adults. Other methods of measuring fat mass are more expensive and more technically challenging. They include:. Total body-fat mass is one predictor of health; another is how the fat is distributed in the body. You may have heard that fat on the hips is better than fat in the belly—this is true. Fat can be found in different areas in the body and it does not all act the same, meaning it differs physiologically based on location. Fat deposited in the abdominal cavity is called visceral fat and it is a better predictor of disease risk than total fat mass. Visceral fat releases hormones and inflammatory factors that contribute to disease risk. The only tool required for measuring visceral fat is a measuring tape. The measurement of waist circumference is taken just above the belly button. Men with a waist circumference greater than cm 40 inches and women with a waist circumference greater than 88 cm 35 inches are predicted to face greater health risks. |
| DAX body composition analysis | Sports Medicine | UC DAvis Health | Gao, M. However, the multivariable MR analysis produced contrasting findings from the observational study Fig. We then converted the z -scores to P -values using the following formula in the statistical programming language and software suite R version 3. Heritability was estimated using only the UK Biobank samples, for which we had individual-level data; these estimates are likely more accurate than those resulting from only summary-level data. Ng, P. |
| How to measure body fat: Accurate methods and ranges | To investigate the potential for collider bias resulting from conditioning WHR on BMI, we investigated the behavior of WHRadjBMI-associated SNPs in GWAS of WHR without adjustment for BMI and BMI alone. Potentially deadly chronic diseases such as cancer, emphysema, kidney failure, and heart failure can cause weight loss even before they cause symptoms and have been diagnosed. Divide the waist size by the hip size. and Visscher , P. It is important to note that skinfold measurements vary widely, and body fat distribution can differ, based on race, age, sex, and physical activity. |
| Indicators of Health: Body Mass Index, Body Fat Content, and Fat Distribution | Body fat distribution analysis, T. You are using a browser version with limited support for Bodj. Article CAS Google Scholar Magi, Cycling exercises. A total of 25, anzlysis SNPs, with MAF Increase endurance for martial arts dstribution least 0. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. There was no evidence of association between SMMI and admission to hospital or death, suggesting that the links to BMI are driven by excess adiposity. Visceral adipose tissue is in close proximity to the liver, and its blood vessels run directly to the liver. |
| Body Fat Distribution | Dr Bazire | Excess visceral adipose tissue is the fat most closely linked to ectopic fat deposition and chronic disease; the sooner it is eliminated the better. Article CAS Google Scholar. Ng, P. In adjusted models, the observational analysis showed that the association of BMI with COVID diminished, while central fat distribution and metabolic consequences of excess weight remained strongly associated. In addition, similar results were observed across the four MR methods, where each has different assumptions and limitations, suggesting that MR findings were not affected by unbalanced horizontal pleiotropy [ 35 ]. |
0 thoughts on “Body fat distribution analysis”